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[2602.16187] SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks
Summary
The paper presents SIT-LMPC, a novel algorithm for safe information-theoretic learning model predictive control tailored for robots performing iterative tasks in uncertain environments.
Why It Matters
This research addresses the critical need for robust and safe control strategies in robotics, particularly for applications requiring iterative learning in complex environments. The proposed algorithm enhances performance while ensuring safety, which is essential for advancing autonomous systems.
Key Takeaways
- SIT-LMPC balances safety and optimality in control strategies.
- The algorithm utilizes an adaptive penalty method for safety assurance.
- It leverages previous iteration data to improve performance through normalizing flows.
- Designed for GPU execution, SIT-LMPC enables efficient real-time optimization.
- Benchmark tests confirm its effectiveness in satisfying system constraints.
Computer Science > Robotics arXiv:2602.16187 (cs) [Submitted on 18 Feb 2026] Title:SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks Authors:Zirui Zang, Ahmad Amine, Nick-Marios T. Kokolakis, Truong X. Nghiem, Ugo Rosolia, Rahul Mangharam View a PDF of the paper titled SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks, by Zirui Zang and 5 other authors View PDF HTML (experimental) Abstract:Robots executing iterative tasks in complex, uncertain environments require control strategies that balance robustness, safety, and high performance. This paper introduces a safe information-theoretic learning model predictive control (SIT-LMPC) algorithm for iterative tasks. Specifically, we design an iterative control framework based on an information-theoretic model predictive control algorithm to address a constrained infinite-horizon optimal control problem for discrete-time nonlinear stochastic systems. An adaptive penalty method is developed to ensure safety while balancing optimality. Trajectories from previous iterations are utilized to learn a value function using normalizing flows, which enables richer uncertainty modeling compared to Gaussian priors. SIT-LMPC is designed for highly parallel execution on graphics processing units, allowing efficient real-time optimization. Benchmark simulations and hardware experiments demonstrate that SIT-LMPC iteratively improves system performance while robust...
